Machine Learning for Turbulence Modeling: 
Recent years have known a surge in machine learning-based models for modeling turbulence. Since it is expensive to resolve all relevant scales, some methods were developed that only solve large scales explicitly and separately model sub-grid scales (SGS). Recently, [1]} used multi-agent reinforcement learning to learn the dissipation coefficient of the Smagorinsky SGS model [2]} using as reward the recovery of the statistical properties of Direct Numerical Simulations (DNS). [3]} used MLPs to represent sub-grid processes in clouds and replace previous parametrization models in a global general circulation model. In the same fashion, [4]} used MLPs to learn DNS sub-grid scale (SGS) stresses using as input filtered flow variables in a turbulent channel flow. [5]} use MLPs to predicts the apparent sources of heat and moisture using coarse-grained data and use a multi-step loss to optimize their model.[6]} used one-layer CNNs to learn the spatial filter in LES methods and the temporal filter in RANS as well as the turbulent terms. A UNet [7]} is then used as a decoder to get the flow velocity. [8]} predict future frames by deforming the input sequence according to the advection-diffusion equation and apply it to Sea-Surface Temperature forecasting.
